



'''simple docstring'''

import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	Union[str, Any]:  # picklable for multiprocessing
		return x.sum()






def 				a  (     UpperCamelCase_    :    Optional[Any]			)       ->	int:  # picklable for multiprocessing
		return i + 1

@dataclass
class 							a__:
			a_	: int
			a_	: str





class 							a__( snake_case__     ):






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     ={}
					snake_case__	     =[]
					snake_case__	     =1
					snake_case__	     =[1, 2]
					snake_case__	     ={'a': 1, 'b': 2}
					snake_case__	     ={'a': [1, 2], 'b': [3, 4]}
					snake_case__	     ={'a': {'1': 1}, 'b': 2}
					snake_case__	     ={'a': 1, 'b': 2, 'c': 3, 'd': 4}
					snake_case__	     ={}
					snake_case__	     =[]
					snake_case__	     =2
					snake_case__	     =[2, 3]
					snake_case__	     ={'a': 2, 'b': 3}
					snake_case__	     ={'a': [2, 3], 'b': [4, 5]}
					snake_case__	     ={'a': {'1': 2}, 'b': 3}
					snake_case__	     ={'a': 2, 'b': 3, 'c': 4, 'd': 5}
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase						)					,   _UpperCAmelCase						)

					snake_case__	     =2
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)

					snake_case__	     ={'a': np.eye(2						), 'b': np.zeros(3						), 'c': np.ones(2						)}
					snake_case__	     ={'a': 2, 'b': 0, 'c': 2}
					snake_case__	     ={
					    'a': np.eye(2						).astype(_UpperCAmelCase						),
					    'b': np.zeros(3						).astype(_UpperCAmelCase						),
					    'c': np.ones(2						).astype(_UpperCAmelCase						),
					}
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   map_numpy=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(
					    {k: v.tolist() for k, v in map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   map_numpy=_UpperCAmelCase						).items()}					,   {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,   )
					self.assertEqual(map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   map_numpy=_UpperCAmelCase					,   num_proc=_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(
					    {k: v.tolist() for k, v in map_nested(_UpperCAmelCase					,   _UpperCAmelCase					,   map_numpy=_UpperCAmelCase					,   num_proc=_UpperCAmelCase						).items()}					,   {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,   )
					with self.assertRaises(_UpperCAmelCase						):  # can't pickle a local lambda
							map_nested(lambda _UpperCAmelCase						: x + 1					,   _UpperCAmelCase					,   num_proc=_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     ={'a': 1, 'b': 2}
					snake_case__	     ={'a': 3, 'b': 4}
					snake_case__	     ={'a': 5, 'b': 6}
					snake_case__	     =sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]						)
					self.assertEqual(sorted(zip_dict(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)						)					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							str:

					class 							a__:
								a_	: Optional[int]									=      '''bar'''





					snake_case__	     =Foo()
					self.assertEqual(foo.my_attr					,   'bar'						)
					with temporary_assignment(_UpperCAmelCase					,   'my_attr'					,   'BAR'						):
							self.assertEqual(foo.my_attr					,   'BAR'						)
					self.assertEqual(foo.my_attr					,   'bar'						)






@pytest.mark.parametrize(
    'iterable_length, num_proc, expected_num_proc'      ,					[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]      ,					)
def 				a  (     UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    Optional[Any]      ,					UpperCamelCase_    :    Tuple			)       ->	Dict:
		with patch('datasets.utils.py_utils._single_map_nested'			) as mock_single_map_nested, patch(
		    'datasets.parallel.parallel.Pool'			) as mock_multiprocessing_pool:
				snake_case__	     ={f"""{i}""": i for i in range(UpperCamelCase_			)}
				snake_case__	     =map_nested(lambda UpperCamelCase_			: x + 10      ,					UpperCamelCase_      ,					num_proc=UpperCamelCase_      ,					parallel_min_length=16			)
				if expected_num_proc == 1:
						assert mock_single_map_nested.called
						assert not mock_multiprocessing_pool.called
				else:
						assert not mock_single_map_nested.called
						assert mock_multiprocessing_pool.called
						assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc

class 							a__( snake_case__     ):






			@require_tf
			def 						_lowercase  (     self						)							->							int:
					import tensorflow as tf
					from tensorflow.keras import layers

					snake_case__	     =layers.Dense(2						)

					def gen_random_output():
							snake_case__	     =tf.random.uniform((1, 3)						)
							return model(_UpperCAmelCase						).numpy()

					with temp_seed(42					,   set_tensorflow=_UpperCAmelCase						):
							snake_case__	     =gen_random_output()
					with temp_seed(42					,   set_tensorflow=_UpperCAmelCase						):
							snake_case__	     =gen_random_output()
					snake_case__	     =gen_random_output()

					np.testing.assert_equal(_UpperCAmelCase					,   _UpperCAmelCase						)
					self.assertGreater(np.abs(outa - outa						).sum()					,   0						)






			@require_torch
			def 						_lowercase  (     self						)							->							Optional[int]:
					import torch

					def gen_random_output():
							snake_case__	     =torch.nn.Linear(3					,   2						)
							snake_case__	     =torch.rand(1					,   3						)
							return model(_UpperCAmelCase						).detach().numpy()

					with temp_seed(42					,   set_pytorch=_UpperCAmelCase						):
							snake_case__	     =gen_random_output()
					with temp_seed(42					,   set_pytorch=_UpperCAmelCase						):
							snake_case__	     =gen_random_output()
					snake_case__	     =gen_random_output()

					np.testing.assert_equal(_UpperCAmelCase					,   _UpperCAmelCase						)
					self.assertGreater(np.abs(outa - outa						).sum()					,   0						)






			def 						_lowercase  (     self						)							->							int:
					def gen_random_output():
							return np.random.rand(1					,   3						)

					with temp_seed(42						):
							snake_case__	     =gen_random_output()
					with temp_seed(42						):
							snake_case__	     =gen_random_output()
					snake_case__	     =gen_random_output()

					np.testing.assert_equal(_UpperCAmelCase					,   _UpperCAmelCase						)
					self.assertGreater(np.abs(outa - outa						).sum()					,   0						)






@pytest.mark.parametrize('input_data'      ,					[{}]			)
def 				a  (     UpperCamelCase_    :    Optional[int]			)       ->	List[str]:
		snake_case__	     =NestedDataStructure(UpperCamelCase_			).data
		assert output_data == input_data






@pytest.mark.parametrize(
    'data, expected_output'      ,					[
        ({}, []),
        ([], []),
        ('foo', ['foo']),
        (['foo', 'bar'], ['foo', 'bar']),
        ([['foo', 'bar']], ['foo', 'bar']),
        ([[['foo'], ['bar']]], ['foo', 'bar']),
        ([[['foo'], 'bar']], ['foo', 'bar']),
        ({'a': 1, 'b': 2}, [1, 2]),
        ({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
        ({'a': {'1': 1}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
    ]      ,					)
def 				a  (     UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    Optional[Any]			)       ->	int:
		snake_case__	     =NestedDataStructure(UpperCamelCase_			).flatten()
		assert output == expected_output






def 				a  (     )       ->	Tuple:
		snake_case__	     =A(x=1      ,					y='foobar'			)
		snake_case__	     ={'x': 1, 'y': 'foobar'}
		assert asdict(UpperCamelCase_			) == expected_output

		snake_case__	     ={'a': {'b': A(x=10      ,					y='foo'			)}, 'c': [A(x=20      ,					y='bar'			)]}
		snake_case__	     ={'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
		assert asdict(UpperCamelCase_			) == expected_output

		with pytest.raises(UpperCamelCase_			):
				asdict([1, A(x=10      ,					y='foo'			)]			)






def 				a  (     UpperCamelCase_    :    str			)       ->	Tuple:
		return text.split()






def 				a  (     UpperCamelCase_    :    List[str]			)       ->	str:
		yield (time.time(), content)
		time.sleep(2			)
		yield (time.time(), content)






def 				a  (     )       ->	List[Any]:
		with Pool(2			) as pool:
				snake_case__	     =list(iflatmap_unordered(UpperCamelCase_      ,					_split_text      ,					kwargs_iterable=[{'text': 'hello there'}] * 10			)			)
				assert out.count('hello'			) == 10
				assert out.count('there'			) == 10
				assert len(UpperCamelCase_			) == 20

		# check multiprocess from pathos (uses dill for pickling)
		with multiprocess.Pool(2			) as pool:
				snake_case__	     =list(iflatmap_unordered(UpperCamelCase_      ,					_split_text      ,					kwargs_iterable=[{'text': 'hello there'}] * 10			)			)
				assert out.count('hello'			) == 10
				assert out.count('there'			) == 10
				assert len(UpperCamelCase_			) == 20

		# check that we get items as fast as possible
		with Pool(2			) as pool:
				snake_case__	     =[]
				for yield_time, content in iflatmap_unordered(
				    UpperCamelCase_      ,					_aseconds_generator_of_aitems_with_timing      ,					kwargs_iterable=[{'content': 'a'}, {'content': 'b'}]			):
						assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
						out.append(UpperCamelCase_			)
				assert out.count('a'			) == 2
				assert out.count('b'			) == 2
				assert len(UpperCamelCase_			) == 4









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

class 							a__( snake_case__     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							None:
					warnings.warn(
					    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use GLPNImageProcessor instead.'					,   _UpperCAmelCase					,   )
					super().__init__(*_UpperCAmelCase					,   **_UpperCAmelCase						)




